Underwater target recognition based on constant-beamwidth waveform fidelity and interference-suppression

In the presence of interference, the recognition performance of underwater target radiated noise severely degrades due to waveform distortion after array beanforming. Based on constant-beamwidth and interference-suppression, a new method of underwater target recognition algorithm is proposed to improve the fidelity of waveform in this paper. In this method, the second-order cone programming (SOCP) is exploited to deal with signal distortion of broadband constant-beamwidth beamforming. Combining SOCP with auditory filtering techniques, we extract intrinsic features of the targets to effectively distinguish them from each other. Based on array signal simulated from the ship-radiated noise collected by a hydrophone, the results and their analysis demonstrate preliminarily the effectiveness of the proposed algorithm in target classification.